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Cited by 246

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Tumor-targeted nanomedicines for cancer theranostics

Open access
Arranja, Alexandra G., Pathak, Vertika, Lammers, Twan, Shi, Yang·2017·Pharmacological Research

Abstract

Graphical abstract Figure. No caption available. ABSTRACT Chemotherapeutic drugs have multiple drawbacks, including severe side effects and suboptimal therapeutic efficacy. Nanomedicines assist in improving the biodistribution and target accumulation of chemotherapeutic drugs, and are therefore able to enhance the balance between efficacy and toxicity. Multiple types of nanomedicines have been evaluated over the years, including liposomes, polymer‐drug conjugates and polymeric micelles, which rely on strategies such as passive targeting, active targeting and triggered release for improved tumor‐directed drug delivery. Based on the notion that tumors and metastases are highly heterogeneous, it is important to integrate imaging properties in nanomedicine formulations in order to enable non‐invasive and quantitative assessment of targeting efficiency. By allowing for patient pre‐selection, such next generation nanotheranostics are useful for facilitating clinical translation and personalizing nanomedicine treatments.

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Crossref · 10.1016/j.phrs.2016.11.014

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Metadata

Authors
Arranja, A. G., Pathak, V., Lammers, T., & Shi, Y.
Venue
Pharmacological Research
Publisher
Elsevier BV
Published
2017-01
Volume
115
Pages
87-95
ISSN
1043-6618 (ISSN-L)
OpenAlex
W2550375380
Cited by
246
References
14
FWCI
5.26
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Tumor-targeted nanomedicines for cancer theranostics. https://app.cochat.ai/papers/10.1016/j.phrs.2016.11.014
The paper
Arranja, A. G., Pathak, V., Lammers, T., & Shi, Y. (2017). Tumor-targeted nanomedicines for cancer theranostics. Pharmacological Research. https://doi.org/10.1016/j.phrs.2016.11.014

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.